Hostname: page-component-7b9c58cd5d-dkgms Total loading time: 0 Render date: 2025-03-15T16:04:16.027Z Has data issue: false hasContentIssue false
  • English
  • Français

Behavioral welfare economics and risk preferences: a Bayesian approach

Published online by Cambridge University Press:  14 March 2025

Xiaoxue Sherry Gao ,
Glenn W. Harrison Open the ORCID record for Glenn W. Harrison [Opens in a new window]  and
Rusty Tchernis
Xiaoxue Sherry Gao*
Affiliation:
Department of Resource Economics, University of Massachusetts Amherst, Amherst, USA
Glenn W. Harrison*
Affiliation:
Department of Risk Management & Insurance and Center for the Economic Analysis of Risk, Robinson College of Business, Georgia State University, Atlanta, USA School of Economics, University of Cape Town, Cape Town, South Africa
Rusty Tchernis*
Affiliation:
Department of Economics, Andrew Young School of Policy Studies, Georgia State University, Atlanta, USA
*
[email protected]
[email protected]
[email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

We propose the use of Bayesian estimation of risk preferences of individuals for applications of behavioral welfare economics to evaluate observed choices that involve risk. Bayesian estimation provides more systematic control of the use of informative priors over inferences about risk preferences for each individual in a sample. We demonstrate that these methods make a difference to the rigorous normative evaluation of decisions in a case study of insurance purchases. We also show that hierarchical Bayesian methods can be used to infer welfare reliably and efficiently even with significantly reduced demands on the number of choices that each subject has to make. Finally, we illustrate the natural use of Bayesian methods in the adaptive evaluation of welfare.

Keywords

Behavioral welfare economicsBayesian analysisRisk preferencesInsurance

JEL classification

D6: Welfare Economics C11: Bayesian Analysis: General G40: General‡ D81: Criteria for Decision-Making under Risk and Uncertainty
Type
Original Paper
Information
Experimental Economics , Volume 26 , Issue 2 , April 2023 , pp. 273 - 303
Copyright
Copyright © The Author(s), under exclusive licence to Economic Science Association 2022

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Footnotes

Supplementary Information The online version contains supplementary material available at https://doi.org/10.1007/s10683-022-09751-0.

References

Allenby, G. M., & Gintner, J. L. (1995). Using extremes to design products and segment markets. Journal of Marketing Research, 32, 392–403. 10.1177/002224379503200402CrossRefGoogle Scholar
Allenby, G. M., & Rossi, P. E. (1999). Marketing models of consumer heterogeneity. Journal of Econometrics, 89, 57–78. 10.1016/S0304-4076(98)00055-4CrossRefGoogle Scholar
Andersen, S., Harrison, G. W., Lau, M. I., & Rutström, E. E. (2008). Eliciting risk and time preferences. Econometrica, 76(3), 583–618. 10.1111/j.1468-0262.2008.00848.xCrossRefGoogle Scholar
Andersen, S., Harrison, G. W., Lau, M. I., & Rutström, E. E. (2018). Multiattribute utility theory, intertemporal utility, and correlation aversion. International Economic Review, 59(2), 537–555. 10.1111/iere.12279CrossRefGoogle Scholar
Armitage, P. (1985). The search for optimality in clinical trials. International Statistical Review, 53(1), 15–24. 10.2307/1402871CrossRefGoogle Scholar
Bartlett, R. H., Roloff, D. W., Cornell, R. G., Andrews, A. F., Dillon, P. W., & Zwischenberger, J. B. (1985). Extracorporeal circulation in neonatal respiratory failure: A prospective randomized study. Pediatrics, 76(4), 479–487. 10.1542/peds.76.4.479CrossRefGoogle ScholarPubMed
Berry, D. A. (1989). Comment: Ethics and ECMO. Statistical Science, 4(4), 306–310. 10.1214/ss/1177012385CrossRefGoogle Scholar
Berry, D. A., & Fristedt, B. (1985). Bandit problems: Sequential allocation of experiments, Springer.CrossRefGoogle Scholar
Caria, S., Gordon, G., Kasy, M., Quinn, S., Shami, S., & Teytelboym, A. (2021). An adaptive targeted field experiment: Job search assistance for refugees in Jordan. Draft Working Paper. Oxford University, July 2021; available at https://maxkasy.github.io/papers/.Google Scholar
Cavagnaro, D. R., Pitt, M. A., Gonzalez, R., & Myung, J. I. (2013). Optimal decision stimuli for risky choice experiments: An adaptive approach. Management Science, 59(2), 358–375. 10.1287/mnsc.1120.1558CrossRefGoogle ScholarPubMed
Cavagnaro, D. R., Pitt, M. A., Gonzalez, R., & Myung, J. I. (2013). Discriminating among probability weighting functions using adaptive design optimization. Journal of Risk and Uncertainty, 47(3), 255–289. 10.1007/s11166-013-9179-3CrossRefGoogle ScholarPubMed
Chaloner, K., & Verdinelli, I. (1995). Bayesian experimental design: A review. Statistical Science, 10(3), 273–304. 10.1214/ss/1177009939CrossRefGoogle Scholar
Chapman, J., Snowberg, E., Wang, S., & Camerer, C. (2018). Loss attitudes in the U.S. population: Evidence from dynamically optimized sequential experimentation (DOSE). NBER Working Paper 25072, September 2018.Google Scholar
Freedman, B. (1987). Equipoise and the ethics of clinical research. New England Journal of Medicine, 317(3), 141–145. 10.1056/NEJM198707163170304CrossRefGoogle ScholarPubMed
Gao, X. S., Harrison, G. W., & Tchernis, R. (2020). Estimating risk preferences for individuals: A Bayesian analysis. CEAR Working Paper 2020–15, Center for the Economic Analysis of Risk, Robinson College of Business, Georgia State University, 2020.Google Scholar
Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., & Rubin, D. B. (2013). Bayesian data analysis, 3CRC Press 10.1201/b16018CrossRefGoogle Scholar
Glennerster, R. Banerjee, A., & Duflo, E. (2017). The practicalities of running randomized evaluations: Partnerships, measurement, ethics, and transparency. Handbook of field experiments: Volume one, Elsevier.Google Scholar
Hadad, V., Hirshberg, D. A., Zhan, R., Wager, S., & Athey, S. (2021). Confidence intervals for policy evaluation in adaptive experiments. Proceedings of the National Academy of Sciences, 118(15), e2014602118 10.1073/pnas.2014602118CrossRefGoogle ScholarPubMed
Harrison, G. W. (1990). Risk attitudes in first-price auction experiments: A Bayesian analysis. Review of Economics & Statistics, 72, 541–546. 10.2307/2109366CrossRefGoogle Scholar
Harrison, G. W. (2011). Experimental methods and the welfare evaluation of policy lotteries. European Review of Agricultural Economics, 38(3), 335–360. 10.1093/erae/jbr029CrossRefGoogle Scholar
Harrison, G. W. (2019). The behavioral welfare economics of insurance. Geneva Risk & Insurance Review, 44(2), 137–175. 10.1057/s10713-019-00042-yCrossRefGoogle Scholar
Harrison, G. W. Kincaid, H., & Ross, D. (2021). Experimental design and Bayesian interpretation. Modern guide to the philosophy of economics, Elgar.Google Scholar
Harrison, G. W., & Ng, J. M. (2016). Evaluating the expected welfare gain from insurance. Journal of Risk and Insurance, 83(1), 91–120. 10.1111/jori.12142CrossRefGoogle Scholar
Harrison, G. W., & Ng, J. M. (2018). Welfare effects of insurance contract non-performance. Geneva Risk & Insurance Review, 43(1), 39–76. 10.1057/s10713-018-0024-0CrossRefGoogle Scholar
Harrison, G. W., & Ross, D. (2018). Varieties of paternalism and the heterogeneity of utility structures. Journal of Economic Methodology, 25(1), 42–67. 10.1080/1350178X.2017.1380896CrossRefGoogle Scholar
Harrison, G. W., Rutström, E. E. Cox, J. C., & Harrison, G. W. (2008). Risk aversion in the laboratory. Risk aversion in experiments, Emerald, Research in Experimental Economics 10.1016/S0193-2306(08)00003-3Google Scholar
Harrison, G. W., & Rutström, E. E. (2009). Expected utility and prospect theory: One wedding and a decent funeral. Experimental Economics, 12(2), 133–158. 10.1007/s10683-008-9203-7CrossRefGoogle Scholar
Huber, J., & Train, K. (2001). On the similarity of classical and Bayesian estimates of individual mean partworths. Marketing Letters, 12(3), 259–269. 10.1023/A:1011120928698CrossRefGoogle Scholar
Imbens, G. W. (2021). Statistical significance, p-values, and the reporting of uncertainty. Journal of Economic Perspectives, 35(3), 157–174. 10.1257/jep.35.3.157CrossRefGoogle Scholar
Kass, R. E., & Greenhouse, J. B. (1989). Comment: A Bayesian perspective. Statistical Science, 4(4), 310–317. 10.1214/ss/1177012386CrossRefGoogle Scholar
Kasy, M., & Sautmann, A. (2021). Adaptive treatment assignment in experiments for policy choice. Econometrica, 89(1), 113–132. 10.3982/ECTA17527CrossRefGoogle Scholar
Kitagawa, T., & Tetenov, A. (2008). Who should be treated? Empirical welfare maximization methods for treatment choice. Econometrica, 86(2), 591–616. 10.3982/ECTA13288CrossRefGoogle Scholar
Kruschke, J. K. (2015). Doing Bayesian data analysis: A tutorial with R, JAGS, and Stan, 2Academic Press.Google Scholar
Kruschke, J. K., & Liddell, T. M. (2018). The Bayesian new statistics: Hypothesis testing, estimation, meta-analysis, and power analysis from a Bayesian perspective. Psychonomic Bulletin & Review, 25, 178–206. 10.3758/s13423-016-1221-4CrossRefGoogle ScholarPubMed
Kruschke, J. K., Vanpaemel, W. Busemeyer, J. R., Townsend, J. T., Wang, Z. J., & Eidels, A. (2015). Bayesian estimation in hierarchical models. Oxford handbook of computational and mathematical psychology, Oxford University Press.Google Scholar
Leamer, E. E. (1978). Specification searches: Ad hoc inference with nonexperimental data, Wiley.Google Scholar
Lindley, D. V. (1972). Bayesian statistics: A review, Society for Industrial and Applied Mathematics 10.1137/1.9781611970654CrossRefGoogle Scholar
McCulloch, R., Rossi, P. E., Allenby, G. M. Gatsonis, C., Hodges, J. S., Kass, E. E., & Singpurwalla, N. D. (1995). Hierarchical modeling of consumer heterogeneity: An application to targeting. Case studies in Bayesian statistics, volume II, Springer, Lecture Notes in Statistics.Google Scholar
Monroe, B. Harrison, G. W., & Ross, D. (2022). The welfare consequences of individual-level risk preference estimation. Models of risk preferences: Descriptive and normative challenges, Emerald, Research in Experimental Economics.Google Scholar
Nilsson, H., Rieskamp, J., & Wagenmakers, E-J (2011). Hierarchical Bayesian parameter estimation for cumulative prospect theory. Journal of Mathematical Psychology, 55, 84–93. 10.1016/j.jmp.2010.08.006CrossRefGoogle Scholar
Peto, R. (1985). Discussion of papers by J.A. Bather and P. Armitage. International Statistical Review, 53(1), 31–34. 10.2307/1402875CrossRefGoogle Scholar
Prelec, D. (1998). The probability weighting function. Econometrica, 66, 497–527. 10.2307/2998573CrossRefGoogle Scholar
Quiggin, J. (1982). A theory of anticipated utility. Journal of Economic Behavior & Organization, 3(4), 323–343. 10.1016/0167-2681(82)90008-7CrossRefGoogle Scholar
Ray, D., Golovin, D., Krause, A., & Camerer, C. (2019). Bayesian rapid optimal adaptive design (BROAD): Method and application distinguishing models of risky choice. OSF Preprints, 10.31219/osf.io/utvbzCrossRefGoogle Scholar
Regier, D. A., Ryan, M., Phimister, E., & Marra, C. A. (2009). Bayesian and classical estimation of mixed logit: an application to genetic testing. Journal of Health Economics, 28(3), 598–610.CrossRefGoogle ScholarPubMed
Rossi, P. E., & Allenby, G. M. (1993). A Bayesian approach to estimating household parameters. Journal of Marketing Research, 30, 171–182. 10.1177/002224379303000204CrossRefGoogle Scholar
Rossi, P. E., Allenby, G. M., & McCulloch, R. (2005). Bayesian statistics and marketing, Wiley 10.1002/0470863692CrossRefGoogle Scholar
Royall, R. (1989). Comment. Statistical Science, 4(4), 318–319. 10.1214/ss/1177012387CrossRefGoogle Scholar
Teele, D. L., & Teele, D. (2014). Reflections on the ethics of field experiments. Field experiments and their critics: Essays on the uses and abuses of experimentation in the social sciences, Yale University Press.Google Scholar
Toubia, O., Johnson, E., Evgeniou, T., & Delquié, P. (2013). Dynamic experiments for estimating preferences: An adaptive method of eliciting time and risk parameters. Management Science, 59(3), 613–640. 10.1287/mnsc.1120.1570CrossRefGoogle Scholar
Train, K. (2009). Discrete choice methods with simulation, 2Cambridge University Press.Google Scholar
Ware, J. H. (1989). Investigating therapies of potentially great benefit: ECMO. Statistical Science, 4(4), 298–306.Google Scholar
Yaari, M. E. (1987). The dual theory of choice under risk. Econometrica, 55(1), 95–115. 10.2307/1911158CrossRefGoogle Scholar
Yusuf, S., Peto, R., Lewis, J., Collins, R., & Sleight, P. (1985). Beta blockade during and after mycoardial infarction: An overview of the randomized trials. Progress in Cardiovascular Diseases, 28(5), 335–371. 10.1016/S0033-0620(85)80003-7CrossRefGoogle Scholar
Supplementary material: File

Gao et al. supplementary material

Online Appendices
Download Gao et al. supplementary material(File)
File 1.7 MB